Juan holds a BEng (Mechatronics) and is undertaking a PhD in robotics and autonomous systems at the Queensland University of Technology (QUT), Australia.
His primary interests comprise autonomous Small Unmanned Aerial Vehicle (sUAV) decision-making, machine learning and computer vision for sUAV remote sensing, with a focus on hyperspectral and high-resolution image processing. Juan has worked for research projects in biosecurity, environment monitoring and time-critical applications such as land search and rescue to find lost people in collapsed buildings and bushlands.
– Autonomous Systems
– Reinforcement Learning
– Remote Sensing
– Search and Rescue
– Hyperspectral image processing
–  CSIRO Data61 PhD Scholarship – Commonwealth Scientific and Industrial Research Organisation (CSIRO)
–  CSIRO Data61 Top up Scholarship – Commonwealth Scientific and Industrial Research Organisation (CSIRO)
- J. Sandino, F. Vanegas, F. Gonzalez, and F. Maire, “Autonomous UAV navigation for active perception of targets in uncertain and cluttered environments,” in Aerospace Conference, Big Sky, MT, USA: IEEE, 2020, pp. 1–12. (https://eprints.qut.edu.au/200148/)
- J. Sandino, F. Gonzalez, K. Mengersen, and K. J. Gaston, “UAVs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands,” Sensors, vol. 18, no. 2, p. 605, 2018. doi: 10.3390/s18020605 (https://doi.org/10.3390/s18020605).
- J. Sandino, G. Pegg, F. Gonzalez, and G. Smith, “Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence,” Sensors, vol. 18, no. 4, p. 944, 2018. doi: 10.3390/s18040944 (https://doi.org/10.3390/s18040944).
Personal website: https://juansandino.com/
Google Scholar: https://scholar.google.com/citations?user=K6Vw3bYAAAAJ&hl=en
- Aerial Mapping of Forests Affected by Pathogens using UAVs, Hyperspectral Sensors and Artificial Intelligence: Myrtle Rust
- Autonomous UAV decision making under environment and target detection uncertainty
- Detection and mapping of exotic weeds using UAS and machine learning: Bitou Bush Case Study
- Developing pest risk models of Buffel Grass using Unmanned Aerial Systems and Statistical methods